GOMS is a well-known model that has been successfully used in predicting the performance of human-computer interaction, identifying usability problems and improving user-interface design. The focus of GOMS on the indi...
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GOMS is a well-known model that has been successfully used in predicting the performance of human-computer interaction, identifying usability problems and improving user-interface design. The focus of GOMS on the individual user, however, explains why it has not been applied in the groupware context. We were inspired by GOMS to define a model that describes collaborative tasks in a formal way. We illustrate the application of the model by applying it to the design of a collaborative tool for software engineering requirements negotiation.
Membrane capacitance and membrane conductance values are reported for insulin secreting cells (primary β-cells and INS-1 insulinoma cells), determined using the methods of dielectrophoresis and electrorotation. The m...
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Membrane capacitance and membrane conductance values are reported for insulin secreting cells (primary β-cells and INS-1 insulinoma cells), determined using the methods of dielectrophoresis and electrorotation. The membrane capacitance value of 12.57 (±1.46)mFm-2, obtained for β-cells, and the values from 9.96 (±1.89)mFm-2 to 10.65 (±2.1)mFm-2, obtained for INS-1 cells, fall within the range expected for mammalian cells. The electrorotation results for the INS-1 cells lead to a value of 36 (±22)Sm-2 for the membrane conductance associated with ion channels, if values in the range 2-3nS are assumed for the membrane surface conductance. This membrane conductance value falls within the range reported for INS cells obtained using the whole-cell patch-clamp technique. However, the total 'effective' membrane conductance value of 601 (±182) Sm-2 obtained for the INS-1 cells by dielectrophoresis is significantly larger (by a factor of around three) than the values obtained by electrorotation. This could result from an increased membrane surface conductance, or increased passive conduction of ions through membrane pores, induced by the larger electric field stresses experienced by cells in the dielectrophoresis experiments.
As the need for comparing genomes of different species has grown dramatically with the fast progress of the Human Genome Project, the evolution at the level of whole genomes has attracted more and more attention from ...
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Each stage of the sewage treatment process emits odor causing compounds and these compounds may vary from one location in a sewage treatment works to another. In order to determine the boundaries of legal standards, r...
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Eliciting and re-using knowledge within an organization requires a very structured communication process among its employees in order to avoid misunderstanding and confusion. The transfer of knowledge among actors can...
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Background: Public health departments in the United States are beginning to gain timely access to health data, often as soon as one day after a visit to a health care facility. Consequently, new approaches to outbreak...
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Background: Public health departments in the United States are beginning to gain timely access to health data, often as soon as one day after a visit to a health care facility. Consequently, new approaches to outbreak surveillance are being developed. When cases cluster geographically, an analysis of their spatial distribution can facilitate outbreak detection. Our method focuses on detecting perturbations in the distribution of pair-wise distances among all patients in a geographical region. Barring outbreaks, this distribution can be quite stable over time. We sought to exemplify the method by measuring its cluster detection performance, and to determine factors affecting sensitivity to spatial clustering among patients presenting to hospital emergency departments with respiratory syndromes. Methods: The approach was to (1) define a baseline spatial distribution of home addresses for a population of patients visiting an emergency department with respiratory syndromes using historical data;(2) develop a controlled feature set simulation by inserting simulated outbreak data with varied parameters into authentic background noise, thereby creating semisynthetic data;(3) compare the observed with the expected spatial distribution;(4) establish the relative value of different alarm strategies so as to maximize sensitivity for the detection of clustering;and (5) measure factors which have an impact on sensitivity. Results: Overall sensitivity to detect spatial clustering was 62%. This contrasts with an overall alarm rate of less than 5% for the same number of extra visits when the extra visits were not characterized by geographic clustering. Clusters that produced the least number of alarms were those that were small in size (10 extra visits in a week, where visits per week ranged from 120 to 472), diffusely distributed over an area with a 3 km radius, and located close to the hospital (5 km) in a region most densely populated with patients to this hospital. Near perfect
Most edge detection algorithms include three main stages: smoothing, differentiation, and labeling. In this paper, we evaluate the performance of algorithms in which competitive learning is applied first to enhance ed...
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ISBN:
(纸本)1604238216
Most edge detection algorithms include three main stages: smoothing, differentiation, and labeling. In this paper, we evaluate the performance of algorithms in which competitive learning is applied first to enhance edges, followed by an edge detector to locate the edges. In this way, more detailed and relatively more unbroken edges can be found as compared to the results when an edge detector is applied alone. The algorithms compared are K-Means, SOM and SOGR for clustering, and Canny and GED for edge detection. Perceptionally, best results were obtained with the GED-SOGR algorithm. The SOGR is also considerably simpler and faster than the SOM algorithm.
In this paper, we discuss edge detection by first using a clustering algorithm followed by a known edge detection filter such as Canny or Generalized Edge Detector (GED). We developed a new clustering method called Se...
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The objective of our research was to find the best approach to handle missing attribute values in data sets describing preterm birth provided by the Duke University. Five strategies were used for filling in missing at...
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By finding the corresponding shortest edit distance between two signed gene permutations, we can know the smallest number of insertions, deletions, and inversions required to change on string of genes into another, wh...
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